In recent advancements in Conversational Large Language Models (LLMs), a
concerning trend has emerged, showing that many new base LLMs experience a
knowledge reduction in their foundational capabilities following Supervised
Fine-Tuning (SFT). This process often leads to issues such as forgetting or a
decrease in the base model's abilities. Moreover, fine-tuned models struggle to
align with user preferences, inadvertently increasing the generation of toxic
outputs when specifically prompted. To overcome these challenges, we adopted an
innovative approach by completely bypassing SFT and directly implementing
Harmless Reinforcement Learning from Human Feedback (RLHF). Our method not only
preserves the base model's general capabilities but also significantly enhances
its conversational abilities, while notably reducing the generation of toxic
outputs. Our approach holds significant implications for fields that demand a
nuanced understanding and generation of responses, such as customer service. We
applied this methodology to Mistral, the most popular base model, thereby
creating Mistral-Plus. Our validation across 11 general tasks demonstrates that
Mistral-Plus outperforms similarly sized open-source base models and their
corresponding instruct versions. Importantly, the conversational abilities of
Mistral-Plus were significantly improved, indicating a substantial advancement
over traditional SFT models in both safety and user preference alignment.

通过采用无害的来自人类反馈的强化学习方法，我们绕过了监督微调，直接应用于 Mistral，从而创建了 Mistral-Plus，它不仅保留了基础模型的通用能力，还显著增强了其对话能力，并大幅减少了有毒输出的生成。